CN116501446B - Kubernetes cluster deployment method and system, and electronic device - Google Patents

Abstract

The disclosure relates to a Kubernetes cluster deployment method and system, and an electronic device, wherein the method comprises the following steps: in the Kubernetes, based on a first target Operator, monitoring whether an updated custom resource configuration file exists, wherein the custom resource configuration file is used for executing target Kubernetes cluster operation; and under the condition that the updated custom resource configuration file exists, executing the target Kubernetes cluster operation according to a task pipeline corresponding to the predefined target Kubernetes cluster operation. The embodiment of the disclosure can rapidly execute the target Kubernetes cluster operation, simplify the Kubernetes cluster deployment process and effectively improve the Kubernetes cluster deployment efficiency.

Description

Kubernetes cluster deployment method and system, and electronic device

Technical Field

The present disclosure relates to the field of computer technology, and in particular to a Kubernetes cluster deployment method and system, and an electronic device.

Background technique

In cloud computing data center scenarios, Kubernetes clusters and graphics processing units (GPUs) are usually installed through shell scripts according to corresponding documents and guidelines. However, since Kubernetes clusters involve multiple Kubernetes components and multiple GPU application types, a quick and easy Kubernetes cluster deployment method is urgently needed.

Summary of the invention

The present invention discloses a Kubernetes cluster deployment method and system, and a technical solution for electronic equipment.

According to one aspect of the present disclosure, a Kubernetes cluster deployment method is provided, including: in Kubernetes, based on a first target Operator, listening to whether there is an updated custom resource configuration file, wherein the custom resource configuration file is used to execute a target Kubernetes cluster operation; in the case of listening to the existence of the updated custom resource configuration file, executing the target Kubernetes cluster operation according to a predefined task pipeline corresponding to the target Kubernetes cluster operation.

In a possible implementation, a task pipeline includes multiple sequentially arranged functional modules, each functional module includes at least one task; for any task, the task includes a target operation location in the Kubernetes and an operation that needs to be performed at the target operation location.

In a possible implementation, executing the target Kubernetes cluster operation according to the predefined task pipeline corresponding to the target Kubernetes cluster operation includes: when the target Kubernetes cluster operation is a containerized GPU driver operation, determining the GPU application type corresponding to the containerized GPU driver operation, wherein the containerized GPU driver operation is executing a GPU driver installation operation or a GPU driver upgrade operation in a target container; determining the task pipeline corresponding to the containerized GPU driver operation according to the GPU application type corresponding to the containerized GPU driver operation; and executing the containerized GPU driver operation in the target container according to the task pipeline corresponding to the containerized GPU driver operation.

In a possible implementation, the GPU application type corresponding to the containerized GPU driver operation includes: an exclusive GPU type and a shared GPU type.

In a possible implementation, executing the target Kubernetes cluster operation according to the predefined task pipeline corresponding to the target Kubernetes cluster operation includes: when the target Kubernetes cluster operation is a virtualized driver operation, determining a GPU application type corresponding to the virtualized GPU driver operation, wherein the virtualized GPU driver operation is executing a GPU driver installation operation or a GPU driver upgrade operation in a target virtual machine; determining a task pipeline corresponding to the virtualized GPU driver operation according to the GPU application type corresponding to the virtualized GPU driver operation; and executing the virtualized GPU driver operation in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driver operation.

In a possible implementation, the GPU application type corresponding to the virtualized GPU driver operation includes: virtualized GPU and pass-through GPU.

In one possible implementation, the target Kubernetes cluster operation includes at least one of the following: cluster installation operation, cluster upgrade operation, cluster deletion operation, node addition operation, certificate update operation, GPU driver installation operation for multiple GPU application types, GPU driver upgrade operation for multiple GPU application types, multiple GPU device plug-in installation operation, Kubernetes component installation operation, and Kubernetes component upgrade operation.

In a possible implementation, the method further includes: determining the updated custom resource configuration file based on user selection, wherein the user selection includes: version configuration, network plug-in configuration, storage configuration, node configuration, and GPU application type configuration.

In a possible implementation, the method further includes: upon detecting the existence of an updated custom resource configuration file, sending the custom resource configuration file to the API Server in the Kubernetes; after executing the target Kubernetes cluster operation, requesting the API Server to update the execution status of the custom resource configuration file.

In a possible implementation manner, the method further includes: setting and managing a master node in the Kubernetes based on the second target Operator.

According to one aspect of the present disclosure, a Kubernetes cluster deployment system is provided, including: a first target Operator, used to listen in Kubernetes to see whether there is an updated custom resource configuration file, wherein the custom resource configuration file is used to execute a target Kubernetes cluster operation; an operation execution module, used to, when the existence of the updated custom resource configuration file is monitored, execute the target Kubernetes cluster operation according to a predefined task pipeline corresponding to the target Kubernetes cluster operation, wherein a task pipeline includes a plurality of sequentially arranged functional modules.

In a possible implementation, each functional module includes at least one task; for any task, the task includes a target operation location in the Kubernetes and an operation that needs to be performed at the target operation location.

In one possible implementation, the operation execution module is specifically used to: when the target Kubernetes cluster operation is a containerized GPU driver operation, determine the GPU application type corresponding to the containerized GPU driver operation, wherein the containerized GPU driver operation is to perform a GPU driver installation operation or a GPU driver upgrade operation in a target container; determine the task pipeline corresponding to the containerized GPU driver operation according to the GPU application type corresponding to the containerized GPU driver operation; and execute the containerized GPU driver operation in the target container according to the task pipeline corresponding to the containerized GPU driver operation.

In a possible implementation, the GPU application type corresponding to the containerized GPU driver operation includes: an exclusive GPU type and a shared GPU type.

In one possible implementation, the operation execution module is specifically used to: when the target Kubernetes cluster operation is a virtualized driver operation, determine the GPU application type corresponding to the virtualized GPU driver operation, wherein the virtualized GPU driver operation is to perform a GPU driver installation operation or a GPU driver upgrade operation in the target virtual machine; determine the task pipeline corresponding to the virtualized GPU driver operation according to the GPU application type corresponding to the virtualized GPU driver operation; and execute the virtualized GPU driver operation in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driver operation.

In a possible implementation, the GPU application type corresponding to the virtualized GPU driver operation includes: virtualized GPU and pass-through GPU.

In one possible implementation, the target Kubernetes cluster operation includes at least one of the following: cluster installation operation, cluster upgrade operation, cluster deletion operation, node addition operation, certificate update operation, GPU driver installation operation for multiple GPU application types, GPU driver upgrade operation for multiple GPU application types, multiple GPU device plug-in installation operation, Kubernetes component installation operation, and Kubernetes component upgrade operation.

In a possible implementation, the system further includes: a user configuration module, used to determine the updated custom resource configuration file based on user selection, wherein the user selection includes: version configuration, network plug-in configuration, storage configuration, node configuration, and GPU application type configuration.

In a possible implementation, the system also includes: a sending module, which is used to send the custom resource configuration file to the API Server in the Kubernetes when it is detected that there is an updated custom resource configuration file; a status update module, which is used to request the API Server to update the execution status of the custom resource configuration file after executing the target Kubernetes cluster operation.

In a possible implementation, the system further includes: a second target Operator, configured to set up and manage a master node in the Kubernetes.

According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

In the disclosed embodiment, in Kubernetes, based on the first target Operator, it is monitored whether there is an updated custom resource configuration file, wherein the custom resource configuration file is used to perform the target Kubernetes cluster operation; when it is monitored that there is a custom resource configuration file, the target Kubernetes cluster operation is performed according to the predefined task pipeline corresponding to the target Kubernetes cluster operation. Based on the first target Operator and the predefined task pipeline corresponding to the target Kubernetes cluster operation, the target Kubernetes cluster operation can be quickly executed, simplifying the Kubernetes cluster deployment process and effectively improving the Kubernetes cluster deployment efficiency.

It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

FIG1 shows a flow chart of a Kubernetes cluster deployment method according to an embodiment of the present disclosure;

FIG2 shows a block diagram of a Kubernetes cluster deployment architecture according to an embodiment of the present disclosure;

FIG3 shows a flowchart of GPU deployment of multiple application types in a Kubernetes cluster according to an embodiment of the present disclosure;

FIG4 shows a block diagram of a Kubernetes cluster deployment system according to an embodiment of the present disclosure;

FIG5 shows a block diagram of an electronic device according to an embodiment of the present disclosure.

Detailed ways

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

In cloud computing data center scenarios, Kubernetes clusters and GPU devices of various application types are usually installed through shell scripts according to the following process.

Step 1: Install the Kubernetes cluster. Choose a suitable Kubernetes installation solution (for example, Kubeadm, Kubespray, Kops, etc.), install and configure according to the corresponding documentation and guides, and ensure that the Kubernetes components on all nodes (for example, kubelet, kube-proxy, kube-controller-manager, etc.) are installed and running correctly.

Step 2: Install GPU drivers for different application types. Install the corresponding drivers for GPU devices of various application types. According to the manufacturer and model of each GPU device, follow the corresponding documents and guidelines to ensure that the drivers for GPU devices of various application types are correctly installed and configured.

Step 3: Install and configure the GPU device plugin. Install and configure the corresponding GPU device plugin for various application types of GPU devices. The GPU device plugin is responsible for detecting and managing GPU devices of various application types and registering them to the Kubernetes Application Programming Interface (API) server. Follow the corresponding documentation and examples of the GPU device plugin to ensure that the GPU device plugin is correctly installed and configured.

Although the installation solutions of Kubernetes clusters and GPU devices of various application types in the related art can support workloads in various GPU application scenarios (e.g., graphics rendering, image processing, AI, high-performance computing, etc.), there are still some shortcomings and technical problems that need to be solved. First, driver and version compatibility: There are compatibility issues between different GPU devices and drivers. It is a challenge to ensure that the selected GPU driver is compatible with the Kubernetes version and operating system used. It is necessary to carefully consider the compatibility between the GPU driver and the Kubernetes version, and ensure that the GPU driver and Kubernetes version of the selected solution support the required GPU devices. Second, management and configuration complexity: The complexity of installing and configuring GPU devices of various application types is high. Each application type GPU device may require different drivers and GPU device plug-ins, and it is necessary to ensure that these components are installed and configured correctly. Managing Kubernetes clusters of GPU devices of various application types also requires additional efforts and technical knowledge. Third, Kubernetes cluster installation and upgrade. There are many Kubernetes components and dependencies involved in the installation of Kubernetes clusters, and there are certain version requirements. To solve the above technical problems, it is necessary to pay close attention to the version compatibility, configuration adjustment and performance optimization of key Kubernetes components such as GPU drivers, GPU device plug-ins, resource schedulers and container runtimes. In addition, it is also necessary to continuously monitor and test the performance, stability and security of the Kubernetes system.

In order to meet the requirements of efficient deployment of Kubernetes clusters in different application scenarios of cloud computing data centers, the present disclosure provides a Kubernetes cluster deployment method. The Kubernetes cluster deployment method provided by the present disclosure is described in detail below.

FIG1 shows a flow chart of a Kubernetes cluster deployment method according to an embodiment of the present disclosure. The method can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The method can be implemented by a processor calling a computer-readable instruction stored in a memory. Alternatively, the method can be executed by a server. As shown in FIG1 , the method includes:

In step S11, in Kubernetes, based on the first target Operator, it is monitored whether there is an updated custom resource configuration file, wherein the custom resource configuration file is used to execute the target Kubernetes cluster operation.

The first target Operator here can be developed based on the Operator open source tool and used for Kubernetes deployment. The specific development process and development method of the first target Operator can be implemented according to actual conditions, and this disclosure does not specifically limit this.

When Kubernetes deployment is required, a custom resource definition (CRD) configuration file can be created or updated based on the requirements, and the custom resource definition configuration file created or updated is used to perform the target Kubernetes cluster operation. The method of creating or updating the custom resource configuration file will be described in detail later in conjunction with the possible implementation of the present disclosure, and will not be repeated here.

The first target Operator monitors whether there is an updated custom resource configuration file based on the List Watch mechanism.

In step S12, when an updated custom resource configuration file is detected, the target Kubernetes cluster operation is executed according to the predefined task pipeline corresponding to the target Kubernetes cluster operation.

In the Kubernetes architecture, task pipelines corresponding to different Kubernetes cluster operations can be pre-defined to improve the execution efficiency of Kubernetes cluster operations.

When the first target Operator detects that there is an updated custom resource configuration file, the target Kubernetes cluster operation can be quickly executed according to the predefined task pipeline corresponding to the target Kubernetes cluster operation to complete the Kubernetes cluster deployment. The following text will describe in detail the process of pre-defining task pipelines corresponding to different Kubernetes cluster operations and how to execute the target Kubernetes cluster operation according to the predefined task pipeline corresponding to the target Kubernetes cluster operation in combination with possible implementation methods of the present disclosure, which will not be repeated here.

In the disclosed embodiment, based on the first target Operator and the predefined task pipeline corresponding to the target Kubernetes cluster operation, the target Kubernetes cluster operation can be quickly executed, the Kubernetes cluster deployment process is simplified, and the Kubernetes cluster deployment efficiency is effectively improved.

In a possible implementation, a task pipeline includes multiple sequentially arranged functional modules, each functional module includes at least one task; for any task, the task includes a target operation location in Kubernetes and an operation that needs to be performed at the target operation location.

In the Kubernetes architecture, task pipelines corresponding to different Kubernetes cluster operations are pre-defined. A task pipeline includes multiple sequentially arranged functional modules, each of which includes at least one task. For any task, the task includes the target operation location in Kubernetes and an operation that needs to be performed at the target operation location.

Through the pre-defined task pipelines corresponding to different Kubernetes cluster operations, you can quickly use SSH and Kubernetes API to perform corresponding Kubernetes cluster operations on the host and Kubernetes cluster, thereby realizing host resource allocation and configuration management in the Kubernetes cluster.

Figure 2 shows a block diagram of the Kubernetes cluster deployment architecture according to an embodiment of the present disclosure. As shown in Figure 2, the architecture includes three parts, the top layer includes a command module (Command), a first Operator, a configuration file (ConfigurationFile), a custom resource configuration file (CRD), the middle layer includes different Kubernetes cluster operations (as shown in Figure 2, cluster installation (Install cluster), adding nodes (Add nodes), cluster upgrade (Upgrade cluster), cluster deletion (Delete cluster), update certificates (Renew certificates), etc.), a task management module (Task manager), a configuration management module (Configuration manager), and the bottom layer includes predefined task pipelines (Pipeline), SSH, cache (Cache), logs (Log), etc. corresponding to each Kubernetes cluster operation.

As shown in Figure 2, a task pipeline (Pipeline) (for example, the task pipeline corresponding to the cluster installation operation, the task pipeline corresponding to the node addition operation, etc.) includes multiple sequentially arranged functional modules (functional module 1 (Module1), functional module 2 (Module 2), to functional module n (Module n)), and a task pipeline includes the complete execution process of executing a cluster operation; a functional module is a module with specific and complete functions, and a functional module includes at least one task (Task); a task includes a target operation location in Kubernetes, and an operation (Action) that needs to be performed at the target operation location. For example, a task includes fields such as Action, Hosts, Retry, and Parallel, where the Action field indicates the Action managed by the task, the Hosts field indicates the target operation location for executing the Action in Kubernetes, the Retry field indicates whether to retry, and the Parallel field indicates whether to process in parallel; an operation (Action) is the most basic unit, indicating an operation that needs to be performed at the target operation location.

In addition to the above-mentioned recording forms, the specific form of a task pipeline can also be set to other forms according to actual needs, and the present disclosure does not make specific limitations on this.

In a possible implementation, the method further includes: determining an updated custom resource configuration file based on user selection, wherein the user selection includes: version configuration, network plug-in configuration, storage configuration, node configuration, and GPU application type configuration.

When Kubernetes deployment is required, the updated custom resource configuration file is determined based on the version configuration, network plug-in configuration, storage configuration, node configuration, GPU application type configuration and other configuration information selected by the user under specific application scenarios and user needs, thereby effectively implementing customized configuration that meets user needs and solving version compatibility issues.

In a possible implementation, the method further includes: upon detecting the existence of an updated custom resource configuration file, sending the custom resource configuration file to an API Server in Kubernetes; and after executing the target Kubernetes cluster operation, requesting the API Server to update the execution status of the custom resource configuration file.

When the first target Operator detects that there is an updated custom resource configuration file, it sends the custom resource configuration file to the API Server in Kubernetes and determines that the execution status of the custom resource configuration file is the initialization status; after executing the target Kubernetes cluster operation corresponding to the custom resource configuration file, it requests the API Server to update the execution status of the custom resource configuration file to the completed status. Based on the first target Operator and the API Server, the monitoring and status management of the custom resource configuration file are effectively realized.

The Kubernetes cluster deployment method based on the embodiment of the present disclosure can support GPU deployment of various application types. The detailed process of GPU deployment of various application types is described below.

In a possible implementation, a target Kubernetes cluster operation is executed according to a predefined task pipeline corresponding to the target Kubernetes cluster operation, including: when the target Kubernetes cluster operation is a containerized GPU driver operation, determining a GPU application type corresponding to the containerized GPU driver operation, wherein the containerized GPU driver operation is executing a GPU driver installation operation or a GPU driver upgrade operation in a target container; determining a task pipeline corresponding to the containerized GPU driver operation according to the GPU application type corresponding to the containerized GPU driver operation; and executing the containerized GPU driver operation in the target container according to the task pipeline corresponding to the containerized GPU driver operation.

The task pipelines corresponding to the containerized GPU driver operations of different GPU application types are predefined, so that the deployment of containerized GPU devices of different GPU application types can be quickly implemented based on the task pipelines.

FIG3 shows a flowchart of GPU deployment of multiple application types in a Kubernetes cluster according to an embodiment of the present disclosure. As shown in FIG3 , based on the user demand for deploying a containerized GPU device, a corresponding custom resource configuration file (CRD) is created or modified, and the custom resource configuration file is used to perform a containerized GPU driver operation. The first target Operator monitors in real time whether there is an updated custom resource configuration file based on the ListWatch mechanism, and after monitoring the existence of an updated custom resource configuration file, sends the updated custom resource configuration file to the API Server. Detect whether the target Kubernetes cluster operation corresponding to the updated custom resource configuration file is a containerized GPU driver operation (GPU driver installation or GPU driver upgrade). If the target Kubernetes cluster operation is a containerized GPU driver operation, determine the GPU application type corresponding to the containerized GPU driver operation. According to the GPU application type corresponding to the containerized GPU driver operation, determine the task pipeline corresponding to the containerized GPU driver operation. Then, according to the task pipeline corresponding to the containerized GPU driver operation, quickly execute the containerized GPU driver operation in the target container. Finally, request the API Server to update the execution status of the custom resource configuration file to a completed state.

The specific operation process of the task pipeline corresponding to the containerized GPU driver operation may include: driver download, node expulsion (the node is the node where the target container is located), node scheduling prohibition, driver installation, and role allocation (i.e. setting the GPU application type of the target container).

In a possible implementation, the GPU application types corresponding to the containerized GPU driver operation include: an exclusive GPU type and a shared GPU type.

As shown in FIG3 , based on the above method, it is possible to deploy an exclusive GPU type containerized GPU device (GPU) and a shared GPU type containerized GPU device (sGPU) in a Kubernetes cluster.

In a possible implementation, a target Kubernetes cluster operation is executed according to a predefined task pipeline corresponding to the target Kubernetes cluster operation, including: when the target Kubernetes cluster operation is a virtualized driver operation, determining a GPU application type corresponding to the virtualized GPU driver operation, wherein the virtualized GPU driver operation is executing a GPU driver installation operation or a GPU driver upgrade operation in a target virtual machine; determining a task pipeline corresponding to the virtualized GPU driver operation according to the GPU application type corresponding to the virtualized GPU driver operation; and executing the virtualized GPU driver operation in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driver operation.

The task pipelines corresponding to the virtualized GPU driver operations of different GPU application types are predefined, so that the deployment of virtualized GPU devices of different GPU application types can be quickly implemented based on the task pipelines.

As shown in FIG3 , based on the user demand for deploying a virtualized GPU device, a corresponding custom resource configuration file is created or modified, and the custom resource configuration file is used to perform a virtualized GPU driver operation. The first target Operator monitors in real time whether there is an updated custom resource configuration file based on the List Watch mechanism. After monitoring the existence of an updated custom resource configuration file, the updated custom resource configuration file is sent to the API Server, and the execution state of the custom resource configuration file is determined to be an initialization state. Detect whether the target Kubernetes cluster operation corresponding to the updated custom resource configuration file is a virtualized GPU driver operation (GPU driver installation or GPU driver upgrade). If the target Kubernetes cluster operation is a virtualized GPU driver operation, determine the GPU application type corresponding to the virtualized GPU driver operation. According to the GPU application type corresponding to the virtualized GPU driver operation, determine the task pipeline corresponding to the virtualized GPU driver operation. Then, according to the task pipeline corresponding to the virtualized GPU driver operation, quickly execute the virtualized GPU driver operation in the target virtual machine. Finally, request the API Server to update the execution state of the custom resource configuration file to a completed state.

The specific operation process of the task pipeline corresponding to the virtualized GPU driver operation may include: driver download, node eviction (the node is the node where the target virtual machine is located), driver installation, etc.

In a possible implementation, the GPU application types corresponding to the virtualized GPU driver operation include: virtualized GPU and pass-through GPU.

As shown in FIG3 , based on the above method, it is possible to deploy an exclusive GPU type virtualized GPU device (passthrough) and a shared GPU type virtualized GPU device (vGPU) in a Kubernetes cluster.

In one possible implementation, the target Kubernetes cluster operation includes at least one of the following: cluster installation operation, cluster upgrade operation, cluster deletion operation, node addition operation, certificate update operation, GPU driver installation operation for multiple GPU application types, GPU driver upgrade operation for multiple GPU application types, multiple GPU device plug-in installation operation, Kubernetes component installation operation, and Kubernetes component upgrade operation.

Based on the first target Operator and the predefined target Kubernetes cluster operation corresponding task pipeline, you can quickly implement cluster installation, cluster upgrade, cluster deletion, certificate update, component installation, component upgrade and other operations in the Kubernetes cluster to achieve rapid and unified deployment of the Kubernetes cluster, and you can also add nodes to expand the Kubernetes cluster.

In a possible implementation, the method further includes: setting and managing a master node in Kubernetes based on the second target Operator.

The second target Operator here can be developed based on the Operator open source tool and is used to set up and manage the master node in Kubernetes. The specific development process and development method of the second target Operator can be implemented according to actual conditions, and this disclosure does not specifically limit this.

Based on the second target Operator, multiple master nodes can be set up in the Kubernetes cluster, and by monitoring the health status of the current master node, when the health status of the current master node is found to be abnormal and cannot work normally, it can be quickly switched to other master nodes to ensure the high availability of the Kubernetes cluster.

In the disclosed embodiment, in Kubernetes, based on the first target Operator, it is monitored whether there is an updated custom resource configuration file, wherein the custom resource configuration file is used to perform the target Kubernetes cluster operation; when it is monitored that there is a custom resource configuration file, the target Kubernetes cluster operation is performed according to the predefined task pipeline corresponding to the target Kubernetes cluster operation. Based on the first target Operator and the predefined task pipeline corresponding to the target Kubernetes cluster operation, the target Kubernetes cluster operation can be quickly executed, simplifying the Kubernetes cluster deployment process and effectively improving the Kubernetes cluster deployment efficiency.

It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

In addition, the present disclosure also provides a Kubernetes cluster deployment system, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any Kubernetes cluster deployment method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to in the corresponding records of the method part and will not be repeated here.

FIG4 shows a block diagram of a Kubernetes cluster deployment system according to an embodiment of the present disclosure. As shown in FIG4 , the system 40 includes:

The first target Operator 41 is used to monitor whether there is an updated custom resource configuration file in Kubernetes, wherein the custom resource configuration file is used to perform target Kubernetes cluster operations;

The operation execution module 42 is used to execute the target Kubernetes cluster operation according to the predefined task pipeline corresponding to the target Kubernetes cluster operation when an updated custom resource configuration file is detected, wherein a task pipeline includes multiple sequentially arranged functional modules.

In a possible implementation, each functional module includes at least one task;

For any task, the task includes the target operation location in Kubernetes and an operation that needs to be performed at the target operation location.

In a possible implementation, the operation execution module 42 is specifically configured to:

In the case where the target Kubernetes cluster operation is a containerized GPU driver operation, determining a GPU application type corresponding to the containerized GPU driver operation, wherein the containerized GPU driver operation is to perform a GPU driver installation operation or a GPU driver upgrade operation in the target container;

Determine a task pipeline corresponding to the containerized GPU driver operation according to the GPU application type corresponding to the containerized GPU driver operation;

According to the task pipeline corresponding to the containerized GPU driver operation, the containerized GPU driver operation is executed in the target container.

In a possible implementation, the GPU application types corresponding to the containerized GPU driver operation include: an exclusive GPU type and a shared GPU type.

In a possible implementation, the operation execution module 42 is specifically configured to:

In the case where the target Kubernetes cluster operation is a virtualized driver operation, determining a GPU application type corresponding to the virtualized GPU driver operation, wherein the virtualized GPU driver operation is to perform a GPU driver installation operation or a GPU driver upgrade operation in the target virtual machine;

Determine a task pipeline corresponding to the virtualized GPU driver operation according to a GPU application type corresponding to the virtualized GPU driver operation;

The virtualized GPU driver operation is executed in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driver operation.

In a possible implementation, the GPU application types corresponding to the virtualized GPU driver operation include: virtualized GPU and pass-through GPU.

In one possible implementation, the target Kubernetes cluster operation includes at least one of the following: cluster installation operation, cluster upgrade operation, cluster deletion operation, node addition operation, certificate update operation, GPU driver installation operation for multiple GPU application types, GPU driver upgrade operation for multiple GPU application types, multiple GPU device plug-in installation operation, Kubernetes component installation operation, and Kubernetes component upgrade operation.

In a possible implementation, the system 40 further includes:

The user configuration module is used to determine the updated custom resource configuration file based on user selection, wherein the user selection includes: version configuration, network plug-in configuration, storage configuration, node configuration, and GPU application type configuration.

In a possible implementation, the system 40 further includes:

The sending module is used to send the custom resource configuration file to the API Server in Kubernetes when an updated custom resource configuration file is detected;

The status update module is used to request the API Server to update the execution status of the custom resource configuration file after executing the target Kubernetes cluster operation.

In a possible implementation, the system 40 further includes:

The second target Operator is used to set up and manage the master node in Kubernetes.

This method has a specific technical connection with the internal structure of the computer system, and can solve the technical problem of how to improve the hardware computing efficiency or execution effect (including reducing the amount of data storage, reducing the amount of data transmission, increasing the hardware processing speed, etc.), thereby obtaining the technical effect of improving the internal performance of the computer system in accordance with the laws of nature.

In some embodiments, the functions or modules included in the system provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

The embodiment of the present disclosure also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method is implemented. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

The embodiment of the present disclosure also proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

The electronic device may be provided as a terminal, a server, or a device in other forms.

FIG5 shows a block diagram of an electronic device according to an embodiment of the present disclosure. Referring to FIG5 , the electronic device 1900 may be provided as a server or a terminal device. Referring to FIG5 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each of which corresponds to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Microsoft's server operating system (Windows Server ^™ ), Apple's graphical user interface-based operating system (Mac OS X ^™ ), a multi-user multi-process computer operating system (Unix ^™ ), a free and open source Unix-like operating system (Linux ^™ ), an open source Unix-like operating system (FreeBSD ^™ ), or the like.

In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

The present disclosure may be a system, a method and/or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing/processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device.

The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

Various aspects of the present disclosure are described herein with reference to the flowcharts and/or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and/or block diagram and the combination of each box in the flowchart and/or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, set clear and prominent signs to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to collect his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs/information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (18)

1. A Kubernetes cluster deployment method, comprising:

in the Kubernetes, based on a first target Operator, monitoring whether an updated custom resource configuration file exists, wherein the custom resource configuration file is used for executing target Kubernetes cluster operation;

Under the condition that the updated user-defined resource configuration file exists, executing the target Kubernetes cluster operation according to a task pipeline corresponding to the predefined target Kubernetes cluster operation;

A task pipeline comprises a plurality of sequentially arranged functional modules, wherein each functional module comprises at least one task;

For any task, the task comprises a target operation position in the Kubernetes and an operation required to be executed at the target operation position;

The executing the target Kubernetes cluster operation according to the task pipeline corresponding to the predefined target Kubernetes cluster operation includes:

Determining a GPU application type corresponding to the containerized GPU driving operation under the condition that the target Kubernetes cluster operation is the containerized GPU driving operation, wherein the containerized GPU driving operation is to execute GPU driving installation operation or GPU driving upgrading operation in a target container;

Determining a task pipeline corresponding to the containerized GPU driving operation according to the GPU application type corresponding to the containerized GPU driving operation;

And executing the containerized GPU driving operation in the target container according to the task pipeline corresponding to the containerized GPU driving operation.

2. The method of claim 1, wherein the containerizing GPU-driven operation corresponding GPU application types comprises: exclusive GPU type, shared GPU type.

3. The method of claim 1, wherein the performing the target Kubernetes cluster operation according to a task pipeline corresponding to the predefined target Kubernetes cluster operation comprises:

Determining a GPU application type corresponding to the virtualized GPU driving operation under the condition that the target Kubernetes cluster operation is the virtualized GPU driving operation, wherein the virtualized GPU driving operation is to execute GPU driving installation operation or GPU driving upgrading operation in a target virtual machine;

Determining a task pipeline corresponding to the virtualized GPU driving operation according to the GPU application type corresponding to the virtualized GPU driving operation;

and executing the virtualized GPU driving operation in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driving operation.

4. A method according to claim 3, wherein virtualizing the GPU application type corresponding to the GPU-driven operation comprises: virtualized GPU, direct GPU.

5. The method of claim 1, wherein the target Kubernetes cluster operation comprises at least one of: cluster installation operation, cluster upgrading operation, cluster deletion operation, node addition operation, certificate updating operation, multiple GPU application type GPU driving installation operation, multiple GPU application type GPU driving upgrading operation, multiple GPU equipment plug-in installation operation, kubernetes component installation operation and Kubernetes component upgrading operation.

6. The method according to claim 1, wherein the method further comprises:

determining the updated custom resource profile based on a user selection, wherein the user selection comprises: version configuration, network plug-in configuration, storage configuration, node configuration, GPU application type configuration.

7. The method according to claim 1, wherein the method further comprises:

If the user-defined resource configuration file with the update is monitored, the user-defined resource configuration file is sent to an application program interface server API SERVER in the Kubernetes;

And after the target Kubernetes cluster operation is executed, requesting API SERVER to update the execution state of the custom resource configuration file.

8. The method according to claim 1, wherein the method further comprises:

the master node in the Kubernetes is set and managed based on a second target Operator.

9. A Kubernetes cluster deployment system, comprising:

A first target Operator, configured to monitor whether an updated custom resource configuration file exists in Kubernetes, where the custom resource configuration file is used to execute a target Kubernetes cluster operation;

The operation execution module is used for executing the target Kubernetes cluster operation according to a task pipeline corresponding to the predefined target Kubernetes cluster operation under the condition that the updated user-defined resource configuration file is monitored, wherein one task pipeline comprises a plurality of sequentially arranged functional modules;

each functional module comprises at least one task;

For any task, the task comprises a target operation position in the Kubernetes and an operation required to be executed at the target operation position;

the operation execution module is specifically configured to:

Determining a GPU application type corresponding to the containerized GPU driving operation under the condition that the target Kubernetes cluster operation is the containerized GPU driving operation, wherein the containerized GPU driving operation is to execute GPU driving installation operation or GPU driving upgrading operation in a target container;

Determining a task pipeline corresponding to the containerized GPU driving operation according to the GPU application type corresponding to the containerized GPU driving operation;

And executing the containerized GPU driving operation in the target container according to the task pipeline corresponding to the containerized GPU driving operation.

10. The system of claim 9, wherein the containerized GPU-driven operation corresponding GPU application types comprise: exclusive GPU type, shared GPU type.

11. The system according to claim 9, wherein the operation execution module is specifically configured to:

Determining a GPU application type corresponding to the virtualized GPU driving operation under the condition that the target Kubernetes cluster operation is the virtualized GPU driving operation, wherein the virtualized GPU driving operation is to execute GPU driving installation operation or GPU driving upgrading operation in a target virtual machine;

Determining a task pipeline corresponding to the virtualized GPU driving operation according to the GPU application type corresponding to the virtualized GPU driving operation;

and executing the virtualized GPU driving operation in the target virtual machine according to the task pipeline corresponding to the virtualized GPU driving operation.

12. The system of claim 11, wherein the virtualized GPU driver operation corresponds to a GPU application type comprising: virtualized GPU, direct GPU.

13. The system of claim 9, wherein the target Kubernetes cluster operation comprises at least one of: cluster installation operation, cluster upgrading operation, cluster deletion operation, node addition operation, certificate updating operation, multiple GPU application type GPU driving installation operation, multiple GPU application type GPU driving upgrading operation, multiple GPU equipment plug-in installation operation, kubernetes component installation operation and Kubernetes component upgrading operation.

14. The system of claim 9, wherein the system further comprises:

a user configuration module, configured to determine the updated custom resource configuration file based on a user selection, where the user selection includes: version configuration, network plug-in configuration, storage configuration, node configuration, GPU application type configuration.

15. The system of claim 9, wherein the system further comprises:

a sending module, configured to send the custom resource configuration file to API SERVER in the Kubernetes when the custom resource configuration file with the update is monitored;

And the state updating module is used for requesting API SERVER to update the execution state of the custom resource configuration file after the target Kubernetes cluster operation is executed.

16. The system of claim 9, wherein the system further comprises:

And a second target Operator is used for setting and managing the master nodes in the Kubernetes.

17. An electronic device, comprising:

A processor;

a memory for storing processor-executable instructions;

Wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 8.

18. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 8.